Health insurance plans offer comprehensive security when it comes to extensive medical-related expenditures. Yet, when filing an insurance claim for any service, various reasons may lead to a rejection of the claim, ushering in a financial burden on the client and a loss of monetary compensation for the service provider. Thus, to avoid potential embarrassment and payment lapses, healthcare organizations claiming insurance require a mechanism to forecast their clients’ eligibility.
We were approached by a California-based client who had created the world’s first cross-functional revenue intelligence software. The massive scope of the product required a sophisticated fusion of data engineering services and machine learning expertise. The client approached us to bolster their existing product with cutting-edge analytical functionality.
Our experts have worked extensively with parties related to the healthcare industry and hence are proficient in tasks dealing with healthcare, thus, this was a fairly workable situation for us. We approached the project with the primary aim of improving claim acceptance and rejection prediction.
Handling a cross-functional team meant effective management of the members and the tasks involved. Accordingly, the project work was divided into sprints. Feedback sessions were held to discuss project progress and make necessary changes based on new requirements if any.
This module used data from the AWS Redshift warehouse to train the revenue intelligence model periodically. As a result, rules were created. Each rule resulted from a thorough examination of the claims and the material submitted indicating claim denial. For each sort of claim refused, the rules generator also produced suggestions.
This module was implemented to read the rules created by the rules creator to check if the rule existed in the database. If a rule was new, it was ingested into the database.
This master script automated the rule-creation and rule-ingestion procedure, and the algorithm ran the show. At each stage of the process, the algorithm sent Slack notifications so that they could be remotely monitored from any device without a console or dashboard.
This tool was used to validate claims. New validators could also be rapidly integrated into this service, enhancing the tool’s scalability and maintainability.
Rules evolve throughout time; therefore, the product needed to recognize those that were no longer relevant. Thus, this form of predictive modeling enhanced the revenue intelligence software by revalidating those rules.
The client had clear expectations for improving their existing machine-learning models. Thus the assignment challenged our experts to put their best foot forward. The intensive knowledge of machine learning and the creation of the prediction model worked in the best interest of both parties. Our team performed effectively and beyond expectations.
The modules implemented allowed healthcare professionals to carry out rejection predictions with accuracy. The revenue intelligence software boosted patient engagement through accessible treatment options, decreased cash flow cycles through intelligent denial management, and raised revenues through fewer rejections.
This project necessitated periodic revisions because the healthcare sector constantly changes, impacting insurance policies. Based on fresh modifications and specifications, our team of professionals has been working on updating the product.